import gradio as gr
from gradio_client import Client, handle_file
import os


# Define your Hugging Face token (make sure to set it as an environment variable)
HF_TOKEN = os.getenv("HF_TOKEN")  # Replace with your actual token if not using an environment variable

# Initialize the Gradio Client for the specified API
client = Client("mangoesai/Elections_Comparing_Agent_V2", hf_token=HF_TOKEN)

client_name = ['2016 Election','2024 Election', 'Comparison two years']



def stream_chat_with_rag(
    message: str,
    history: list,
    client_name: str
):
    print(f"Message: {message}")
    print(f"History: {history}")

    # Build the conversation prompt including system prompt and history
    conversation = f"{system_prompt}\n\nFor Client: {client_name}\n"
    
    # Add previous conversation history
    for user_input, assistant_response in history:
        conversation += f"User: {user_input}\nAssistant: {assistant_response}\n"
    
    # Add the current user message
    conversation += f"User: {message}\nAssistant:"

    # Call the API with the user's process_query
    question = message
    #answer = client.predict(question=question, api_name="/run_graph")
    answer = client.predict(
    	query= message,
		election_year=client_name,
		api_name="/process_query"
    )

    # Debugging: Print the raw response
    print("Raw answer from API:")
    print(answer)


    return answer


    


# Title for the application
TITLE = "<h1 style='text-align:center;'>Reddit Election Q&A agent v0.1</h1>"

# Create the Gradio Blocks interface
with gr.Blocks(css=CSS) as demo:
    gr.HTML(TITLE)
    with gr.Tab("Chat"):
        chatbot = gr.Chatbot()  # Create a chatbot interface
        chat_interface = gr.ChatInterface(
            fn=stream_chat_with_rag,
            chatbot=chatbot,
            additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
            additional_inputs=[
                gr.Dropdown(client_name,value="2016 Election",label="Select Election year", render=False,allow_custom_value=True)
            ],
        )


    # with gr.Tab("Process PDF"):
    #     pdf_input = gr.File(label="Upload PDF File")
    #     #select_client_dropdown = gr.Dropdown(client_name, value="rosariarossi", label="Select or Type Client", allow_custom_value=True)
    #     pdf_output = gr.Textbox(label="PDF Result", interactive=False)
    
    #     pdf_button = gr.Button("Process PDF")
    #     pdf_button.click(
    #         process_pdf,
    #         inputs=[pdf_input],  # Pass both PDF and client name is not required 
    #         outputs=pdf_output
    #     )

    # with gr.Tab("Answer with RAG"):
    #     question_input = gr.Textbox(label="Enter Question for RAG")
    #     answer_with_rag_select_client_dropdown = gr.Dropdown(client_name, value="primo", label="Select or Type Client", allow_custom_value=True)
    #     rag_output = gr.Textbox(label="RAG Answer Result", interactive=False)

    #     rag_button = gr.Button("Get Answer")
    #     rag_button.click(
    #         rag_api,
    #         inputs=[question_input,answer_with_rag_select_client_dropdown ],
    #         outputs=rag_output
    #     )
    # with gr.Tab(label="Manage Files"):
    #     with gr.Column():
    #         delete_index_button = gr.Button("Delete All Files")
    #         delete_index_textout = gr.Textbox(label="Deleted Files and Refresh Result")
    #         delete_index_button.click(fn=delete_index, inputs=[],outputs=[delete_index_textout])

# Launch the app
if __name__ == "__main__":
    demo.launch()